Ventricular repolarization indicators in risk stratification of decompensated heart failure patients with ventricular systolic dysfunction

Background: Ventricular repolarization measurement by QTc interval and QT dispersion can recognize high-risk patients. Previous research tended to evaluate the act of repolarization indicators alone but this study aimed to elucidate their prognostic utility before and after modifying confounding parameters in risk stratification of different aspects of prognosis in decompensated heart failure patients with systolic dysfunction. Methods: Data of 98 variables were evaluated to determine their predictive value concerning arrhythmic events, in-hospital, and long-term mortality. Results: From 858 cases that presented with acute heart failure, 19.2% (n=165) were enrolled in the study. During hospitalization, arrhythmic events and cardiac-related mortality occurred in 56(33.9%) and 11(7%) patients, respectively. QTc and QT dispersion were independent predictors of arrhythmia and in-hospital mortality after adjustment of the variables (arrhythmic events: QTc interval OR 1.085, P=0.007, QT dispersion OR 1.077, P=0.007, in-hospital mortality: QTc interval OR 1.116, P=0.009, QT dispersion OR 1.067, P=0.011). After being discharged, they were tracked for 181±56 days. Within the 16 deaths in follow-up time, 6 sudden cardiac deaths were documented. Cox regression, defined QTc as the predictor of all-cause and sudden death mortality (all-cause: HR 1.041, 95% CI 1.015-1.067, P=0.002; sudden death: HR 1.063, 95% CI 1.023-1.105, P=0.002); nevertheless, efforts to demonstrate QT dispersion as the predictor failed. Conclusion: The predictive nature of QT parameters was significant after modification of the variables; therefore, they should be measured for risk stratification of ventricular repolarization arrhythmia and death in decompensated heart failure patients.


Hatamnejad M, et al.
Although the literature has addressed QTc interval or QTd as a predictor of arrhythmia and mortality in acute decompensated or chronic heart failure (4)(5)(6)(7)(8)(9)(10)(11), less has been done to elucidate their prognostic role after adjustment for a variety of confounding factors in a model. According to this aim, we did the study to illuminate their prognostic utility before and after modifying confounding parameters in risk stratification of different aspects of outcomes including (1) arrhythmia and mortality during hospitalization, and (2) longterm mortality in DHF patients with ventricular systolic dysfunction.

Methods
Our work with the prospective design was conducted at Al-Zahra Charity Hospital, a university-affiliated tertiary medical center in Shiraz, from December 2019 to April 2020. The investigation convention has been endorsed by the college ethics committee (ethical code: IR.SUMS.MED.REC.1399.248); the Declaration of Helsinki was preserved in all the study phases. Informed assent was obtained before the participation. Study population: Inclusion criteria for enrollment of the patients presenting with decompensated heart failure symptoms (including dyspnea, fatigue, decreased exercise or physical capacity, anorexia, early satiety, weight loss, weight gain, palpitations, peripheral edema, ascites, and disordered breathing according to ACCF/AHA Guideline (12)), in the study were: age more than 18-years, previously known case of heart failure, class III-IV of New York Heart Association (NYHA), and attendance of systolic dysfunction (ejection fraction (EF) below the 40%). The patient will be excluded on condition of having any final diagnosis except DHF; lack of sinus rhythm in electrocardiogram (ECG); low quality of ECG to interpret; pacemaker, bundle branch block, and other causes of widened QRS complex; other causes related to prolonged QT parameters such as electrolyte imbalance, consuming the drugs that are associated with prolonged QT; recent myocardial infarction; dissatisfaction with participation in the study. Clinical Variables: Information on demographic, clinical, and medical variables was obtained from the emergency ward. Previous medical documents and any other archived data were reviewed to specify the etiology of heart failure, and the initial ECG at the emergency room was recorded for subsequent analysis. A blood sample was sent for laboratory tests of complete blood count, hemostasis tests, cardiac biomarkers, lipid profile, electrolytes, renal and hepatic function. Radiologic studies (chest radiography or high-resolution computed tomography) were applied if necessary. Echocardiography to verify the presence of systolic dysfunction concerning the EF value was done. During hospitalization cardiac monitoring, subsequent ECGs were recorded to identify premature ventricular contraction, ventricular tachycardia, and ventricular fibrillation. During the decompensated stage, the standard treatment was given. A telephone call was made at the end of every 90 days after the patient's discharge to get to know about the clinical condition, hospital readmission, and death. ECG analysis: A prospective analysis of the ECG recorded at the first visit to the emergency room was performed to determine the computer-derived heartbeats, QRS duration, QT, and QTc interval. Heart monitoring and subsequent ECGs were analyzed for the arrhythmic event. ECGs' data were digitally recorded for 8 seconds with a pace of 25 mm/sec and an amplitude of 10mm/mv (cardiax system 4.25.5). Inspecting the ECGs to manually compute the QTd, confirming the accuracy of the computer-based QT parameters by digital caliper, and excluding the patients with bundle branch block from the survey was accomplished. The QT interval is started with the QRS complex and is ended at the T-wave termination (whenever it returns to the isoelectric line). In the presence of the U-wave, end of the QT interval is defined as a point in the nadir of the curve between the T and U waves. Calculating the mean of 3 uninterrupted beats in at least 6 leads to obtain the QT interval, wherever possible, was taken. Correcting QT interval (QTc calculation) was done via Bazett's formula except for individuals with tachycardia that Hodges formula was more appropriate (13). The gap between the highest and lowest value of QT interval is described as QTd. Prolonged QTc interval and QTd have been defined as more than 440 ms and 80ms, respectively (14). Patients were tracked for 181 ± 56 days. Information on endpoints was gathered by telephone call at the end of every 90 days. No cases were dropped during the follow-up. End Points: The initial endpoints were arrhythmic events during the hospital course and all-cause mortality either inhospital or long-term. Arrhythmic events were described by the presence of premature ventricular contraction, ventricular tachycardia, and ventricular fibrillation. The status of the DHF patient shifts to chronic stable heart failure with the proper management and after the discharge. Thus, the type of mortality at the time of the follow-up has been determined by the criteria which were defined in previous investigations of chronic heart failure (15,16). Deaths are classified into 4 kinds: (1) Sudden cardiac death on condition of happening in unconscious condition or within the 1hour after starting any signs or symptoms (2) Progressive heart failure death if it occurs after worsening in the hemodynamic or clinical status (3) Other cardiovascular death refers to the cause which cannot be categorized as sudden cardiac or progressive heart failure death but is associated with cardiac system (4) Noncardiovascular death. The importance of such classification is to specify the secondary endpoint since former research has declared that sudden cardiac death is the only type of longterm mortality which is related to QT parameters (7). Therefore, sudden cardiac death has been considered the secondary endpoint. The two physicians who were uninformed about patients' ECGs distinguished the type of death by reviewing the hospital documents. Statistical Analysis: SPSS V.23.0 software package was utilized to perform the analysis. Categorical data were portrayed by percentage; mean ± SD was used for continuous data. Based on the normality of continuous variable's distribution, Mann-Whitney U test or Student's t-test were applied to compare the statistical significance of the difference between groups of study. Comparisons between binary parameters were made via chi-square. Kaplan-Meier diagrams were drawn to compare the groups' survival based on QT parameters. For this purpose, continuous ECG parameters were transformed into dichotomized variables. As previously defined, QTc and QTd more than 440ms and 80ms, respectively, were considered abnormally prolonged.
The relationship between arrhythmic events and mortality during hospitalization with their indicators was examined by logistic models and illustrated by odds ratios, their 95% confidence interval (CI), and p-values. Parameters related to long-term mortality were established by Cox's proportional hazards model and reported by hazard ratios, their CI, and pvalues. In multivariate analysis to obtain the best model for mortality prediction, backward stepwise (likelihood ratio) and enter methods were utilized. A p-value cut-off of 0.05 to consider the result of the analysis as significant, has been assumed.

Results
The process of choosing the patients is presented in figure  1. Within the time of the study, 858 cases were registered to the emergency ward with the impression of acute heart failure.
Among them, 369 had a previous history of heart failure, NYHA Class III-IV, and systolic dysfunction (EF less than 40%); thus, they were eligible for the study. Two hundred four patients were excluded after the primary assessment due to the following reasons: having any final diagnosis except DHF (n=9), absence of sinus rhythm (n=58), using medications with the potential of affecting QT parameters (n=16), left and right bundle branch block (n=75), pacemaker (n=19), presence of ST-elevation (n=13), inadequate quality for QT parameters analysis (n=6), electrolyte imbalance (n=4), and unwilling to participate (n=4). Thus, data of 165 patients were analyzed.

Demographic and clinical data
Baseline features of the patients are displayed in table 1. The population included 96 males and 69 females with an average age of 65 ± 14.1 years. In 53 patients, current smoking was noted (32.1%); 54 patients announced substance consumption which was most opium (32.7%). The mean EF showed severe systolic dysfunction (23.8%). Most of the patients fell within the overweight range according to BMI (25.1±4.7) and were evenly distributed between NYHA classification III and IV (50.3% vs. 49.7%). Dyspnea, orthopnea, and edema were the most common symptoms which occurred during the decompensation (97.6%, 55.2%, and 46.1%). Respiratory sounds (rales/wheeze 71.5%, diminished breath sound 52.7%), heart sounds (third or fourth heart sound 43.6%, tricuspid or mitral regurgitation murmur 53.9%), and extremities edema (46.1%) should be considered as the most common findings in physical examination. Baseline ECG characteristics, laboratory tests, and radiological findings are shown in table1. Diabetes mellitus 44.2% (63.4% of them had uncontrolled HBA1C level according to 2019 ESC guideline), hypertension 63.6% (46.7% had uncontrolled blood pressure according to 2019 ESC guideline), hyperlipidemia 61.8%, previous history of coronary artery disease 69.1% (57.9% underwent (PCI or CABG)) were the most remarkable comorbidities in them. Heart failure was determined based on 4 etiologies in patients: hypertension (14.6%), valvular disease (7.2%), ischemic heart disease (69.1%), and idiopathic dilated cardiomyopathy (9.1%). Table 2 shows medications that have been used before admission and also during hospitalization. Anti-platelet, anticoagulant, ß-blocking agent, loop diuretic, potassium-sparing diuretic, and statin were the most reported medications before admission and during hospitalization. Table 3 exposes the patient's outcome. Univariate and multivariate analyses were utilized to distinguish the parameters which can predict arrhythmic events, in-hospital, and long-term mortality.    2 Odds ratio calculated by multivariate logistic regression for arrhythmia and in-hospital mortality and its 95 % confidence interval Long-term mortality: Of the 153 patients who were discharged and followed up for an average of 181±56 days, 16(9.7% of all DHF patients) died and 137(83%) subjects survived. At the time of follow-up, no cases did not need to implant a defibrillator or cardiac transplantation. Direct contact with the patients' families or their hospital documents determined the mode of the death: non-cardiac deaths in 4(25%) patients, progressive heart failure deaths in 6(37.5%) patients, sudden deaths in 6 (37.5%) patients, and other cardiac death in none of them. All-cause mortality: Smoking (P=0.045), weight gain (P=0.037), anasarca (P=0.038), diabetes mellitus (P=0.016), hyperthyroidism (P=0.004), QTc interval (P=0.005), and random blood sugar (P=0.034) were proved as significant predictors in univariate analysis of cox proportional hazards regression (table 6). Among them, only anasarca and random blood sugar lost their statistical significance when they were analyzed by the multivariate method of the Cox proportional hazard model (table 6).
Although QTc interval was related to all-cause mortality in both univariate and multivariate analysis (P=0.005 and p<0.001, respectively), attempts to show a significant association between QTd and all-cause mortality failed. The Survival comparison between the groups with normal and prolonged QT parameters by Kaplan-Meier curves verified our results and is shown in figure 2. Age and QTc interval preserved their value as significant predictors in multivariate analysis (P=0.010 and P=0.002, respectively). Similar to all-cause mortality, QTc opposed to QTd has presented itself as an independent predictor of sudden cardiac death (table 6). Lots of factors can prolong QT parameters. Although omitting all of them is not possible for the reason of prevention of decreasing the study population, however the most important of them have been listed as exclusion criteria. Valvular heart disease, renal impairment, and digitalis therapy may have an impact on QT parameters; thus, after ignoring the patients with mentioned parameters, analyses were performed again. No change in previous results was observed.

Discussion
Our outcomes illustrate that adverse events can be predicted with QTc interval and QTd as ventricular repolarization indicators. The unique point of our research is the comprehensive evaluation of repolarization parameters besides the other characteristics which were carefully characterized, in a way that resembles risk stratification design.
Mortality due to inadequate cardiac output can be determined by a variety of factors. Systolic or diastolic cardiac dysfunction and valvular or vascular pathology were used to be the most noticeable factors, but recent efforts have been dedicated to investigating the electrophysiological alterations such as cardiac arrhythmias. The cardiac cycle consists of ventricular depolarization and repolarization, and arrhythmia can occur in both. Ventricular activation or depolarization is represented by QRS complex. Prolonged QRS complex was assumed as ventricular activation dys-synchrony and plays the main role in the prediction of arrhythmic events and mortality (5,(17)(18)(19)(20)(21). In contrast to previous studies, we focused to explore the association between ventricular repolarization and prognostic factors, including arrhythmic events, in-hospital, and long-term mortality; hence, the patients with bundle branch block were excluded and those who had normal QRS (<120 ms) duration was entered. According to this approach, none of the multivariate models suggest QRS duration as a predictor of arrhythmic events, in-hospital, and long-term mortality. Ventricular relaxation or repolarization alterations predispose to lethal arrhythmias in a sequence of events. The variety of heart diseases causes structural and electrical modifications in the ion channels of the myocytes, leading to changes in the myocytes' action potentials, including their refractory period and conduction velocity, which results in heterogeneity and fluctuations in repolarization, promoting lethal arrhythmia (22,23). Morphological remodeling of the histological substrate (myocyte hypertrophy, disarray, fibrosis, etc.), especially ion channel (down-regulation of potassium channels; on the contrary, inactivation of sodium channels, and the release and storage of calcium in the sarcoplasmic reticulum) were found to be the pathological basis of ventricular repolarization inhomogeneity (24). Thus, ventricular repolarization measurement is recommended to stratify arrhythmic events; we analyzed QTc interval duration and QTd for this purpose.
Prolonged QTc interval has been offered as a powerful predictor for ventricular arrhythmia, in-hospital, and longterm mortality in heart failure patients (6,25,26). Temizkan et al. (11) represented a hypothesis indicating that monitoring the QTc interval act as an effective instrument in evaluating the DHF patients in the emergency ward which guides us to make a decision about a patient's discharge or transfer to cardiac care unit. Vaclavik et al. (17) declared that prolonged QTc interval is related to in-hospital mortality, as opposed to long-term mortality.
In some reports, a lack of correlation between QTc interval with arrhythmia, in-hospital mortality, and long-term mortality (7,17) was found in acute and chronic heart failure patients subsequently. Breidthardt et al. (5) in a study on 173 acute established heart failure patients revealed a negative association between the QTc interval and long-term mortality. Overall, outcomes on the predictive value of QTc interval in heart failure are controversial. In the same line with previous studies, we were able to indicate a significant relationship between QTc interval as a predictor of arrhythmic events, inhospital and long-term mortality.
It seems that sole assessment of QT interval to have an arrhythmic risk appraisal is not enough. QTd as a tool that accurately demonstrates unequal action potentials' prolongation, incongruity of the duration of the refractory periods, and the conduction velocities of adjacent myocardial regions, is more advantageous to represent ventricular repolarization disturbance (3). The prognostic utility of QTd about arrhythmic events, in-hospital and long-term mortality in heart failure patients has been confirmed by some projects (4,9,27,28). Padmanabhan et al. (26) determined QT interval dispersion as a predictive parameter of all-cause mortality from a cohort of 2265 patients with reduced EF. Nevertheless, some recent papers have failed to demonstrate the predictive role of QTd for arrhythmic events, in-hospital and long-term mortality in heart failure patients (7,8). Brendorp et al. (29) rejected the predicting role of QTd regarding the all-cause mortality after inspecting the 703 heart failure patients with reduced EF. In our project, effort to corroborate a significant association between QTd with arrhythmic events and inhospital mortality was fruitful. In contrast, using Cox regression to predict long-term mortality (all-cause and sudden death) for QTd failed.
These findings have important clinical benefits. Risk stratification of DHF cases is applicable by these ECG parameters because of their mortality anticipating features; also based on ECG's unique features (the most accessible, frugal, easy to work with, quick responding, repeatable, and objective), it can be more practicable compared to other prognostic tools. Rapid intervention by pharmacological (e.g.levosimendan) or invasional therapeutic approach in high-risk patients recognized by these parameters may be so beneficial (5). Despite its prognostic utility, ECG helps to diagnose DHF, especially in identifying the etiology of decompensation containing arrhythmic or ischemic events, so the guidelines of AHA or ESC recommended the ECG as a primary workup for the management of DHF patients.
Although the research has reached its aim, some restrictions exist. At first, despite the remarkable patients' referral to our hospital (n=858), stringent criteria made our sample size small (n=165, 19.2%), so similar studies with more cases are recommended. Second, baseline computerderived ECG contained all the parameters except QTd; however, QTd was calculated manually by a single expert cardiologist who was uninformed about clinical details and outcomes. Although all efforts were made, the manual calculation may affect results, particularly long-term mortality. However, based on the report by Glancy et al. (30) difference between values of QTd measured by manual and automatic methods is scarce, and errors in manual calculating do not impress the outcomes. Third, uric acid, SGOT, SGPT, HDL, and cholesterol were limiting parameters and were kept out of the multivariate logistic regression model; thus, their impact on QT parameters outcome predictability was not elucidated.
Our research had three specific strengths. First, defining 13 inclusion and exclusion criteria to achieve the purest association between QT's parameters and the outcome made our research more accurate compared to similar works. Second, accompanying factors, including demographic data, signs and symptoms, physical examination findings, comorbidities, past social and medication history, hospital medication, etiology of heart failure, laboratory and radiologic parameters neglected in previous investigations, were analyzed in combination with ECG parameters simultaneously. Third, considering the outcome based on the occurrence of arrhythmic events, in-hospital and long-term mortality is more reliable than separately evaluating them. The present prospective study on decompensated heart failure patients suggests considering QTc interval as a prognosticator of arrhythmic events, in-hospital mortality, and sudden death in the long term. Meanwhile, QT interval dispersion is the determinant factor of arrhythmic events and in-hospital mortality. According to their utility, they should be measured for risk stratification of ventricular repolarization arrhythmia and death in DHF patients in daily clinical practice.